How to spot a bot (or not): The main indicators of online automation, co-ordination and inauthentic activity (First Draft)

First Draft: How to spot a bot (or not): The main indicators of online automation, co-ordination and inauthentic activity. “From talking with academics and researchers, studying the work of others, and carrying out our own investigations, First Draft has put together a list of indicators to help anyone identify suspicious online activity. The list of indicators is broken down by category: the account’s pattern of activity, account information, content posted by the account, and network of other accounts it may be a part of. Within each category are different metrics which are red flags for automation.” Good list, though I’ll note the RB Twitter account is guilty of two of them (posting persistently day and night and posting in multiple languages.)

Get All of Your Bots in a Row: 2018 California Bot Disclosure Law Comes Online Soon (National Law Review)

National Law Review: Get All of Your Bots in a Row: 2018 California Bot Disclosure Law Comes Online Soon. “During the 2016 election, certain Russian operatives used fake social media profiles to influence voters and also created bot accounts to add likes to and share posts across the internet. And more recently, in January 2019, the New York Attorney General and Office of the Florida Attorney General announced settlements with certain entities that sold fake social media engagement, such as followers, likes and views. Moreover, many of the social media platforms have had recent purges of millions of fake accounts. Thus, it’s clear that bots and automated activity on social media platforms has been on everyone’s radar…including state legislators’ too.”

Anatomy of Twitter Bots: Fake Followers (Duo Security)

Duo Security: Anatomy of Twitter Bots: Fake Followers. “We recently presented a technical research paper at Black Hat USA 2018 called Don’t @ Me: Hunting Twitter Bots at Scale. This paper provides an in-depth look at the entire process of gathering a large Twitter dataset and using a practical data science approach to identify automated accounts within that dataset.”